Tuple extraction using dynamically generated extractor classes

ABSTRACT

Systems and methods for extracting tuples using dynamically generated extractor classes are disclosed. In some examples, an optimized tuple extraction class can be dynamically generated to enable more efficient tuple extraction.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of, and claims the benefit andpriority to U.S. application Ser. No. 15/133,449, filed Apr. 20, 2016,entitled “TUPLE EXTRACTION USING DYNAMICALLY GENERATED EXTRACTORCLASSES,” which claims the benefit and priority under 35 U.S.C. 119(e)of U.S. Provisional Application No. 62/244,469, filed Oct. 21, 2015,entitled “TUPLE EXTRACTION USING DYNAMICALLY GENERATED EXTRACTORCLASSES.” This application is also related to U.S. Pat. No. 8,935,293,entitled “FRAMEWORK FOR DYNAMICALLY GENERATING TUPLE AND PAGE CLASSES,”filed on Mar. 2, 2009 and issued on Jan. 13, 2015, the entire contentsof which are incorporated by reference for all purposes.

BACKGROUND

In traditional database systems, data is stored in one or more databasesusually in the form of tables. The stored data is then queried andmanipulated using a data management language such as a structured querylanguage (SQL). For example, a SQL query may be defined and executed toidentify relevant data from the data stored in the database. A SQL queryis thus executed on a finite set of data stored in the database.Further, when a SQL query is executed, it is executed once on the finitedata set and produces a finite static result. Databases are thus bestequipped to run queries over finite stored data sets.

A number of modern applications and systems however generate data in theform of continuous data or event streams instead of a finite data set.Examples of such applications include but are not limited to sensor dataapplications, financial tickers, network performance measuring tools(e.g. network monitoring and traffic management applications),clickstream analysis tools, automobile traffic monitoring, and the like.Such applications have given rise to a need for a new breed ofapplications that can process the data streams. For example, atemperature sensor may be configured to send out temperature readings.

Managing and processing data for these types of event stream-basedapplications involves building data management and querying capabilitieswith a strong temporal focus. A different kind of querying mechanism isneeded that comprises long-running queries over continuous unboundedsets of data. While some vendors now offer product suites geared towardsevent streams processing, these product offerings still lack theprocessing flexibility required for handling today's events processingneeds.

SUMMARY

The following portion of this disclosure presents a simplified summaryof one or more innovations, embodiments, and/or examples found withinthis disclosure for at least the purpose of providing a basicunderstanding of the subject matter. This summary does not attempt toprovide an extensive overview of any particular embodiment or example.Additionally, this summary is not intended to identify key/criticalelements of an embodiment or example or to delineate the scope of thesubject matter of this disclosure. Accordingly, one purpose of thissummary may be to present some innovations, embodiments, and/or examplesfound within this disclosure in a simplified form as a prelude to a moredetailed description presented later.

A further understanding of the nature of and equivalents to the subjectmatter of this disclosure (as well as any inherent or express advantagesand improvements provided) should be realized in addition to the abovesection by reference to the remaining portions of this disclosure, anyaccompanying drawings, and the claims.

In some examples, a method, a system, and a computer-readable medium maybe provided. The method, the system, and/or the computer-readable mediummay comprise generating a first extractor for extracting a first fieldfrom a first event, determining an output event type, generating asecond extractor for extracting subsequent fields from subsequent eventsbased at least in part on the input event type, and implementing thesecond extractor. In some cases, the first extractor may be implementedto determine an input event type, extract the first field from the firstevent, and convert the first field to the output event (for the firstevent). Additionally, in some cases, the second extractor may beimplemented to extract a subsequent field from the subsequent event andconvert the subsequent field to the output event. In some cases, thefirst event and the subsequent events may be received from an inputstream. The method, system, and/or computer-readable medium may alsostore the output event as a tuple and/or receive code that identifies atype conversion for each of a plurality of input events. In someexamples, the second extractor may not determine a subsequent inputevent type for the subsequent events. In some examples, the firstextractor, may be generated based at least in part on the code and/orthe code may be received from an entity associated with at least one ofthe first event or the subsequent events. In some instances, the secondextractor may be implemented for each subsequent event and/or at leastone of the first extractor or the second extractor may be implemented asobject oriented classes of an adapter framework.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to reasonably describe and illustrate those innovations,embodiments, and/or examples found within this disclosure, reference maybe made to one or more accompanying drawings. The additional details orexamples used to describe the one or more accompanying drawings shouldnot be considered as limitations to the scope of any of the claimeddisclosures, any of the presently described embodiments and/or examples,or the presently understood best mode of any innovations presentedwithin this disclosure.

FIG. 1 is a simplified illustration of a system that may incorporate anembodiment of the present disclosure.

FIG. 2 is a block diagram that may incorporate an embodiment of thepresent disclosure.

FIG. 3 is a simplified block diagram of details for implementing anembodiment of the present disclosure.

FIG. 4 is a block diagram that includes example pseudocode forimplementing an embodiment of the present disclosure.

FIG. 5 is a flowchart of a method for implementing an embodiment of thepresent disclosure.

FIG. 6 is a flowchart of a method for implementing an embodiment of thepresent disclosure.

FIG. 7 depicts a simplified diagram of a distributed system forimplementing some of the examples described herein, according to atleast one example.

FIG. 8 is a simplified block diagram of components of a systemenvironment by which services provided by the components of anembodiment system may be offered as cloud services, in accordance withsome of the examples described herein, according to at least oneexample.

FIG. 9 illustrates an exemplary computer system, in which variousembodiments of the present disclosure may be implemented in accordingwith some of the examples described herein, according to at least oneexample.

DETAILED DESCRIPTION

In applications such as stock quote monitoring, automobile trafficmonitoring, and data sensing, data is generated in the form of a streamof events over time. A data stream, also referred to as an event stream,is a real-time, continuous, sequence of events. Examples of sources thatgenerate data streams include sensors and probes (e.g., radio frequencyidentification (RFID) sensors, temperature sensors, etc.) configured tosend a sequence of sensor readings, financial tickers,network-monitoring and traffic-management applications sending networkstatus, click stream analysis tools, and others. The term “events” areused interchangeably with “tuples.” As used herein, tuples of a streamhave the same set of attributes but not necessarily the same attributevalues for those attributes. Each tuple is also associated with aparticular time. A tuple may be considered to be logically similar to asingle row or record in a relational database.

An event processing system typically has an adapter layer for ingestinginput events to the system. The primary responsibility of the adapterlayer is to convert the input event types to tuple types. For convertinginput event types to tuple types, the fields from the input event typeshould be extracted and set to tuple types. While extracting the fields,sometimes type conversion is desired. Since the extractor and the typeconversion logic needs to handle any combination of inputs, the typicalimplementation uses type matching logic using if, else, switch, and mapalong with class types. Since the extraction is done for each inputevent and the type conversion is done for each field in the input event,the type conversion operation is critical.

A continuous data stream (also referred to as an event stream) mayinclude a stream of data or events that may be continuous or unboundedin nature with no explicit end. Logically, an event or data stream maybe a sequence of data elements (also referred to as events), each dataelement having an associated timestamp. A continuous event stream may belogically represented as a bag or set of elements (s, T), where “s”represents the data portion, and “T” is in the time domain. The “s”portion is generally referred to as a tuple or event. An event streammay thus be a sequence of time-stamped tuples or events.

In some aspects, the timestamps associated with events in a stream mayequate to a clock time. In other examples, however, the time associatedwith events in an event stream may be defined by the application domainand may not correspond to clock time but may, for example, berepresented by sequence numbers instead. Accordingly, the timeinformation associated with an event in an event stream may berepresented by a number, a timestamp, or any other information thatrepresents a notion of time. For a system receiving an input eventstream, the events arrive at the system in the order of increasingtimestamps. There could be more than one event with the same timestamp.

In some examples, an event in an event stream may represent anoccurrence of some worldly event (e.g., when a temperature sensorchanged value to a new value, when the price of a stock symbol changed)and the time information associated with the event may indicate when theworldly event represented by the data stream event occurred.

For events received via an event stream, the time information associatedwith an event may be used to ensure that the events in the event streamarrive in the order of increasing timestamp values. This may enableevents received in the event stream to be ordered based upon theirassociated time information. In order to enable this ordering,timestamps may be associated with events in an event stream in anon-decreasing manner such that a later-generated event has a latertimestamp than an earlier-generated event. As another example, ifsequence numbers are being used as time information, then the sequencenumber associated with a later-generated event may be greater than thesequence number associated with an earlier-generated event. In someexamples, multiple events may be associated with the same timestamp orsequence number, for example, when the worldly events represented by thedata stream events occur at the same time. Events belonging to the sameevent stream may generally be processed in the order imposed on theevents by the associated time information, with earlier events beingprocessed prior to later events.

The time information (e.g., timestamps) associated with an event in anevent stream may be set by the source of the stream or alternatively maybe set by the system receiving the stream. For example, in certainembodiments, a heartbeat may be maintained on a system receiving anevent stream, and the time associated with an event may be based upon atime of arrival of the event at the system as measured by the heartbeat.It is possible for two events in an event stream to have the same timeinformation. It is to be noted that while timestamp ordering requirementis specific to one event stream, events of different streams could bearbitrarily interleaved.

An event stream has an associated schema “S,” the schema comprising timeinformation and a set of one or more named attributes. All events thatbelong to a particular event stream conform to the schema associatedwith that particular event stream. Accordingly, for an event stream (s,T), the event stream may have a schema ‘S’ as (<time_stamp>,<attribute(s)>), where <attributes> represents the data portion of theschema and can comprise one or more attributes. For example, the schemafor a stock ticker event stream may comprise attributes <stock symbol>,and <stock price>. Each event received via such a stream will have atime stamp and the two attributes. For example, the stock ticker eventstream may receive the following events and associated timestamps:

  ... (<timestamp_N>, <NVDA,4>) (<timestamp_N+1>, <ORCL,62>)(<timestamp_N+2>, <PCAR,38>) (<timestamp_N+3>, <SPOT,53>)(<timestamp_N+4>, <PDCO,44>) (<timestamp_N+5>, <PTEN,50>) ...

In the above stream, for stream element (<timestamp_N+1>, <ORCL,62>),the event is <ORCL,62> with attributes “stock_symbol” and “stock_value.”The timestamp associated with the stream element is “timestamp_N+1”. Acontinuous event stream is thus a flow of events, each event having thesame series of attributes.

As noted above, a stream may be the principle source of data that CQLqueries may act on. Additionally, as noted, a stream S may be a bag(also referred to as a “multi-set”) of elements (s, T), where “s” is inthe schema of S and “T” is in the time domain. Additionally, streamelements may be tuple-timestamp pairs, which can be represented as asequence of timestamped tuple insertions. In other words, a stream maybe a sequence of timestamped tuples. In some cases, there may be morethan one tuple with the same timestamp. And, the tuples of an inputstream may be requested to arrive at the system in order of increasingtimestamps. Alternatively, a relation (also referred to as a “timevarying relation,” and not to be confused with “relational data,” whichmay include data from a relational database) may be a mapping from thetime domain to an unbounded bag of tuples of the schema R. In someexamples, a relation may be an unordered, time-varying bag of tuples(i.e., an instantaneous relation). In some cases, at each instance oftime, a relation may be a bounded set. It can also be represented as asequence of timestamped tuples that may include insertions, deletes,and/or updates to capture the changing state of the relation. Similar tostreams, a relation may have a fixed schema to which each tuple of therelation may conform. Further, as used herein, a continuous query maygenerally be capable of processing data of (i.e., queried against) astream and/or a relation. Additionally, the relation may reference dataof the stream.

In some examples, a tuple and an event are different terms for the sameconcept. However, the formats may be different. In some cases, an“event” is the term used for input and output of a stream. An event maybe received and/or provided as output of a stream processor. However,the data of the event may be stored in a tuple. As such, a “tuple” maybe a CQL engine construct (object) for storing the data of the events.As such, the properties of an event may become the properties of atuple; however, they may be in a different form. In some examples, thephysical form (e.g., the data structure) may be the biggest differencebetween an event and a tuple. In some examples, the input event or theoutput event may be tied to the input or output system, respectively.However, a tuple may be a generic type of object that can store theattributes, properties, etc. of the events. In memory, sometimes thetuple may take the form of an array. For example, an event may bereceived as XML data, which may be parsed and stored in a differentform. The XML event may include many different data points (elements),for example, time, stock price, etc. Based at least in part on a CQLstatement from a user, only one (or some subset) of the attributes maybe stored in an array as the tuple.

In some cases, the CQL engine may not need to know every property of anincoming event. As an example, a stock symbol event may be received. Thestock symbol event may include many properties, including: time, stockprice, bid price, symbol name, offer price, buy price, etc. However, thetuple may only store some of the values that were selected by the user(e.g., based at least in part on the particular CQL query). As such,only the interesting parts are stored in the tuple.

FIG. 1 depicts a simplified example system or architecture 100 in whichtechniques for dynamically generated extractor classes may beimplemented. In architecture 100, one or more users 102 (e.g., accountholders) may utilize user computing devices 104(1)-(N) (collectively,“user devices 104”) to access one or more service provider computers 106via one or more networks 108. In some aspects, the service providercomputers 106 may also be in communication with one or more streamingdata source computers 110 and/or one or more databases 112 via thenetworks 108. For example, the users 102 may utilize the serviceprovider computers 106 to access or otherwise manage data of thestreaming data source computers 110 and/or the databases 112 (e.g.,queries may be run against either or both of 110, 112). The databases112 may be relational databases, SQL servers, or the like and may, insome examples, manage historical data, event data, relations, archivedrelations, or the like on behalf of the users 102. Additionally, thedatabases 112 may receive or otherwise store data provided by thestreaming data source computers 110. In some examples, the users 102 mayutilize the user devices 104 to interact with the service providercomputers 106 by providing queries (also referred to as “querystatements”) or other requests for data (e.g., historical event data,streaming event data, etc.). Such queries or requests may then beexecuted by the service provider computers 106 to process data of thedatabases 112 and/or incoming data from the streaming data sourcecomputers 110. Further, in some examples, the streaming data sourcecomputers 110 and/or the databases 112 may be part of an integrated,distributed environment associated with the service provider computers106.

In some examples, the networks 108 may include any one or a combinationof multiple different types of networks, such as cable networks, theInternet, wireless networks, cellular networks, intranet systems, and/orother private and/or public networks. While the illustrated examplerepresents the users 102 accessing the service provider computers 106over the networks 108, the described techniques may equally apply ininstances where the users 102 interact with one or more service providercomputers 106 via the one or more user devices 104 over a landlinephone, via a kiosk, or in any other manner. It is also noted that thedescribed techniques may apply in other client/server arrangements(e.g., set-top boxes, etc.), as well as in non-client/serverarrangements (e.g., locally stored applications, etc.).

The user devices 104 may be any type of computing device such as, butnot limited to, a mobile phone, a smart phone, a personal digitalassistant (PDA), a laptop computer, a desktop computer, a thin-clientdevice, a tablet PC, etc. In some examples, the user devices 104 may bein communication with the service provider computers 106 via thenetworks 108, or via other network connections. Further, the userdevices 104 may also be configured to provide one or more queries orquery statements for requesting data of the databases 112 (or other datastores) to be processed.

In some aspects, the service provider computers 106 may also be any typeof computing devices such as, but not limited to, mobile, desktop,thin-client, and/or cloud computing devices, such as servers. In someexamples, the service provider computers 106 may be in communicationwith the user devices 104 via the networks 108, or via other networkconnections. The service provider computers 106 may include one or moreservers, perhaps arranged in a cluster, as a server farm, or asindividual servers not associated with one another. These servers may beconfigured to perform or otherwise host features described hereinincluding, but not limited to, the management of archived relations,configurable data windows associated with archived relations, and/oraccurately counting change events associated with managing archivedrelations described herein. Additionally, in some aspects, the serviceprovider computers 106 may be configured as part of an integrated,distributed computing environment that includes the streaming datasource computers 110 and/or the databases 112.

In one illustrative configuration, the service provider computers 106may include at least one memory 136 and one or more processing units (orprocessor(s)) 138. The processor(s) 138 may be implemented asappropriate in hardware, computer-executable instructions, firmware, orcombinations thereof. Computer-executable instruction or firmwareimplementations of the processor(s) 138 may include computer-executableor machine-executable instructions written in any suitable programminglanguage to perform the various functions described.

The memory 136 may store program instructions that are loadable andexecutable on the processor(s) 138, as well as data generated during theexecution of these programs. Depending on the configuration and type ofservice provider computers 106, the memory 136 may be volatile (such asrandom access memory (RAM)) and/or non-volatile (such as read-onlymemory (ROM), flash memory, etc.). The service provider computers 106 orservers may also include additional storage 140, which may includeremovable storage and/or non-removable storage. The additional storage140 may include, but is not limited to, magnetic storage, optical disks,and/or tape storage. The disk drives and their associatedcomputer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules, andother data for the computing devices. In some implementations, thememory 136 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM),or ROM.

The memory 136, the additional storage 140, both removable andnon-removable, are all examples of computer-readable storage media. Forexample, computer-readable storage media may include volatile ornon-volatile, removable or non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Thememory 136 and the additional storage 140 are all examples of computerstorage media.

The service provider computers 106 may also contain communicationsconnection(s) 142 that allow the identity interface computers 120 tocommunicate with a stored database, another computing device or server,user terminals, and/or other devices on the networks 108. The serviceprovider computers 106 may also include input/output (I/O) device(s)144, such as a keyboard, a mouse, a pen, a voice input device, a touchinput device, a display, one or more speakers, a printer, etc.

Turning to the contents of the memory 136 in more detail, the memory 136may include an operating system 146 and one or more application programsor services for implementing the features disclosed herein including atleast a tuple extractor module 148. As used herein, modules may refer toprogramming modules executed by servers or clusters of servers that arepart of a service. In this particular context, the modules may beexecuted by the servers or clusters of servers that are part of theservice provider computers 106.

FIG. 2 depicts a simplified high level diagram of an event processingsystem 200 that may incorporate an embodiment of the present disclosure.Event processing system 200 may comprise one or more event sources (204,206, 208), an event processing server (EPS) 202 (also referred to as CQService 202) that is configured to provide an environment for processingevent streams, and one or more event sinks (210, 212). The event sourcesgenerate event streams that are received by EPS 202. EPS 202 may receiveone or more event streams from one or more event sources. For example,as shown in FIG. 2, EPS 202 receives an input event stream 214 fromevent source 204, a second input event stream 216 from event source 206,and a third event stream 218 from event source 208. One or more eventprocessing applications (220, 222, and 224) may be deployed on and beexecuted by EPS 202. An event processing application executed by EPS 202may be configured to listen to one or more input event streams, processthe events received via the one or more event streams based uponprocessing logic that selects one or more events from the input eventstreams as notable events. The notable events may then be sent to one ormore event sinks (210, 212) in the form of one or more output eventstreams. For example, in FIG. 2, EPS 202 outputs an output event stream226 to event sink 210, and a second output event stream 228 to eventsink 212. In certain embodiments, event sources, event processingapplications, and event sinks are decoupled from each other such thatone can add or remove any of these components without causing changes tothe other components.

In one embodiment, EPS 202 may be implemented as a Java servercomprising a lightweight Java application container, such as one basedupon Equinox OSGi, with shared services. In some embodiments, EPS 202may support ultra-high throughput and microsecond latency for processingevents, for example, by using JRockit Real Time. EPS 202 may alsoprovide a development platform (e.g., a complete real time end-to-endJava Event-Driven Architecture (EDA) development platform) includingtools (e.g., Oracle CEP Visualizer and Oracle CEP IDE) for developingevent processing applications.

An event processing application is configured to listen to one or moreinput event streams, execute logic (e.g., a query) for selecting one ormore notable events from the one or more input event streams, and outputthe selected notable events to one or more event sources via one or moreoutput event streams. FIG. 2 provides a drilldown for one such eventprocessing application 220. As shown in FIG. 2, event processingapplication 220 is configured to listen to input event stream 218,execute a query 230 comprising logic for selecting one or more notableevents from input event stream 218, and output the selected notableevents via output event stream 228 to event sink 212. Examples of eventsources include, without limitation, an adapter (e.g., JMS, HTTP, andfile), a channel, a processor, a table, a cache, and the like. Examplesof event sinks include, without limitation, an adapter (e.g., JMS, HTTP,and file), a channel, a processor, a cache, and the like.

Although event processing application 220 in FIG. 2 is shown aslistening to one input stream and outputting selected events via oneoutput stream, this is not intended to be limiting. In alternativeembodiments, an event processing application may be configured to listento multiple input streams received from one or more event sources,select events from the monitored streams, and output the selected eventsvia one or more output event streams to one or more event sinks. Thesame query can be associated with more than one event sink and withdifferent types of event sinks.

Due to its unbounded nature, the amount of data that is received via anevent stream is generally very large. Consequently, it is generallyimpractical and undesirable to store or archive all the data forquerying purposes. The processing of event streams requires processingof the events in real time as the events are received by EPS 202 withouthaving to store all the received events data. Accordingly, EPS 202provides a special querying mechanism that enables processing of eventsto be performed as the events are received by EPS 202 without having tostore all the received events.

Event-driven applications are rule-driven and these rules may beexpressed in the form of continuous queries that are used to processinput streams. A continuous query may comprise instructions (e.g.,business logic) that identify the processing to be performed forreceived events including what events are to be selected as notableevents and output as results of the query processing. Continuous queriesmay be persisted to a data store and used for processing input streamsof events and generating output streams of events. Continuous queriestypically perform filtering and aggregation functions to discover andextract notable events from the input event streams. As a result, thenumber of outbound events in an output event stream is generally muchlower than the number of events in the input event stream from which theevents are selected.

Unlike a SQL query that is run once on a finite data set, a continuousquery that has been registered by an application with EPS 202 for aparticular event stream may be executed each time that an event isreceived in that event stream. As part of the continuous queryexecution, EPS 202 evaluates the received event based upon instructionsspecified by the continuous query to determine whether one or moreevents are to be selected as notable events, and output as a result ofthe continuous query execution.

The continuous query may be programmed using different languages. Incertain embodiments, continuous queries may be configured using the CQLprovided by Oracle Corporation and used by Oracle's Complex EventsProcessing (CEP) product offerings. Oracle's CQL is a declarativelanguage that can be used to program queries (referred to as CQLqueries) that can be executed against event streams. In certainembodiments, CQL is based upon SQL with added constructs that supportprocessing of streaming events data.

In one embodiment, an event processing application may be composed ofthe following component types:

(1) One or more adapters that interface directly to the input and outputstream and relation sources and sinks. Adapters are configured tounderstand the input and output stream protocol, and are responsible forconverting the event data into a normalized form that can be queried byan application processor. Adapters may forward the normalized event datainto channels or output streams and relation sinks. Event adapters maybe defined for a variety of data sources and sinks.(2) One or more channels that act as event processing endpoints. Amongother things, channels are responsible for queuing event data until theevent processing agent can act upon it.(2) One or more application processors (or event processing agents) areconfigured to consume normalized event data from a channel, process itusing queries to select notable events, and forward (or copy) theselected notable events to an output channel.(4) One or more beans are configured to listen to the output channel,and are triggered by the insertion of a new event into the outputchannel. In some embodiments, this user code is a plain-old-Java-object(POJO). The user application can make use of a set of external services,such as JMS, Web services, and file writers, to forward the generatedevents to external event sinks.(5) Event beans may be registered to listen to the output channel, andare triggered by the insertion of a new event into the output channel.In some embodiments, this user code may use the Oracle CEP event beanAPI so that the bean can be managed by Oracle CEP.

In one embodiment, an event adapter provides event data to an inputchannel. The input channel is connected to a CQL processor associatedwith one or more CQL queries that operate on the events offered by theinput channel. The CQL processor is connected to an output channel towhich query results are written.

In some embodiments, an assembly file may be provided for an eventprocessing application describing the various components of the eventprocessing application, how the components are connected together, eventtypes processed by the application. Separate files may be provided forspecifying the continuous query or business logic for selection ofevents.

It should be appreciated that system 200 depicted in FIG. 2 may haveother components than those depicted in FIG. 2. Further, the embodimentshown in FIG. 2 is only one example of a system that may incorporate anembodiment of the present disclosure. In some other embodiments, system200 may have more or fewer components than shown in FIG. 2, may combinetwo or more components, or may have a different configuration orarrangement of components. System 200 can be of various types includinga personal computer, a portable device (e.g., a mobile telephone ordevice), a workstation, a network computer, a mainframe, a kiosk, aserver, or any other data processing system. In some other embodiments,system 200 may be configured as a distributed system where one or morecomponents of system 200 are distributed across one or more networks inthe cloud.

The one or more of the components depicted in FIG. 2 may be implementedin software, in hardware, or combinations thereof. In some embodiments,the software may be stored in memory (e.g., a non-transitorycomputer-readable medium), on a memory device, or some other physicalmemory and may be executed by one or more processing units (e.g., one ormore processors, one or more processor cores, one or more GPUs, etc.).

1 Dynamically Generated Extractor Class

FIG. 3 depicts a simplified diagram of an adapter framework 300 thatincludes at least the following components: a transport component 302, amapper component 304, a trigger component 306, a filter component and/oran extractor component 308, a converter component 310, and a changedetector component 312.

In some examples, the transport component 302 may be responsible forreceiving contents (e.g., events) from a source. The contents mayinclude more than one event. Additionally, the mapper component 304 maybe responsible for converting one input type of content to another typethat the system understands and generates events. The mapper componentmay generate the output events directly or create an intermediate javatype and use an extractor component. In some example, the JavaArchitecture for XML Binding (JAXB) may be used in implementing themapper component 304 and JAXB may generate intermediate java classesthat can converted to events by the extractor component 308. In someexamples, the trigger component 306 may invoke the transport component302 to receive content (events) from the source. The trigger component306 allows control of processing source data logic when and what. It canprovide an initial set of the source data and it can schedule extractionof data from the source. In some examples, the adapter 300 only dealswith processing data. Thus, it may be desirable to implement atriggerable interface and follow some protocol to invoke the transportcomponent 302 to receive contents. Some embodiments of the triggercomponents 306 are scheduled trigger, folder monitor trigger, delayloopback trigger, and console input trigger. A scheduled trigger maytrigger on a schedule and/or may support “At,” “Delay,” and “Duration.”A folder monitor trigger may monitor folders and send new/updated filepaths. A delay loopback trigger may support delay load and/or delayrepeat functionality. A console input trigger may retrieve input from aconsole and may be useful in demo scenarios.

In some examples, the extractor component 308 may be configured toextract fields associated with the output type of the mapper component304. The extractor component 308 may be implemented based at least inpart on a dynamically generated extractor class. Having a specificextractor component 308 can enable adapter-specific extraction. Theextractor component 308 can provide simple rules (including “foreach”rules) for extraction and can provide a mapping of properties (e.g.,using a converter). The extractor component 308 may work with generalExtensible Stylesheet Language Transformation (XSLT) processing. Examplerule of 308 includes:

foreach(  AlertEntry:alerts,  ′*; geometry=GeomConverter(polygon,AlertGeocode:geocode/valueNames, AlertGeocode:geocode/values')

That is, for each AlertEntry type in alerts of AlertFeed, the extractorcomponent may copy all properties but polygon, valueNames of geocodeproperty Of AlertGeocodeType, values of geocode property OfAlertGeocodeType passed to GeomConverter which creates a Geometry typeand assign it to geometry.

In some examples, the filter component 308 may be configured to filterevents based at least in part on fields of the events. The filtercomponent 308 may be configured to filter based at least in part onevent property. The filter component 308 can support inclusion or caneasily be expanded to general Boolean expression. The filter component308 and the extractor component 308 may be configured as two separatecomponents or as a single component that both extracts and filters.

In some examples, a converter component 310 may be configured to converttypes of fields. For example, property types may be automaticallyconverted for built-in types. Some geometry types may require moretreatment. For example, they may be spread into multiple properties,they may be complex type such as Extensible Markup Language (XML) orJavaScript Object Notation (JSON), and/or they may need geocodehandling. In some examples, a geometry converter may be configured forreceiving weather data or the like. The arguments may include a list ofgeocode names, a list of geocode values, and an optional list oflongitude/latitude pairs. For each geocode name/value, the adapter maydecode the geocode and get polygon data from a geocode database. Then,the geometry converter may return a list of geometries corresponding tothe geocode name/value pairs.

In some examples, a change detector component 312 may be configured todetect changes in contents/events received by the custom adapter. Thechange detector component 312 may detect changes of inputcontents/events and send events of insert/update/delete kinds. In someexamples, the change detector component 312 may only work withRelation-type data; however, in other examples, it may work with alldata types. The change detector component 312 may be useful for workingwith sending updates from the same datasource (e.g., RSS). In someexamples, the Key column from one of fields of tuples required in orderto detect insert/update/delete kinds. One embodiment of change detectioncomponent is using a hash table datastructure where the old events arestored in the hash table and the Key column is to detectinsert/update/delete kinds. Using a map change detection component, thesystem may handle auto expiration from the time specified in the event.

In many examples, systems that process data from a stream may provide anadapter 300 or allow customers to write their own adapter code to beused in the ingestion system. But, most adapter frameworks don't havemany APIs, or they only deal with the transport layer. Meaning, the APIsmay only be used for instructing the adapter on where to read the datafrom. Some more advanced adapters may be configured with APIs formapping the data. However, the flexible event ingestion frameworkdescribed herein can allow for APIs that can be configured to execute orcontrol several other event ingestion components, some of which havebeen described above.

In one example, in order to handle an RSS data source, it may be helpfulto be able to schedule pulling the data and convert the data into aparticular form (e.g., XML). Sometimes, it may be helpful to extract thesome parts of the content into an appropriate event format.Additionally, RSS content may have a timestamp or time mark, which canenable the event ingestion system to determine which data points arerelevant (e.g., it may not be helpful to process data that is outdated).By having this construct, it is possible to mix and match differentcomponents as needed to flexibly configure the adapter framework to meetparticular needs. For example, a Comma Separated Values (CSV) adaptermay read a CSV file and then convert the CSV format to a tuple format.Similarly, for JavaScript Object Notation (JSON) files, a JSON adaptermay read the JSON file and then convert to a tuple format. But, thatbasically involves two parts already because the CSV file is part of thetransport but CSV is more like a mapper area. So, if it's separated outso the file is at the transport, then components can be mixed andmatched for different formats so the transport and mapper are separatedout (e.g., so that the same code doesn't need to be rewritten).Similarly, for the trigger, the trigger may be configured as a mechanismthat triggers the transport (e.g., an HTTP transport) to read something(e.g., an HTTP file) every particular duration. Alternatively, thetrigger may also be configured to monitor the source and identify whenthe file is new, and trigger the transport to read the new file. Thetrigger may also be activated by a user (e.g., selecting a read of aninput source from a console or other UI).

In some examples, the change detector component 312 may be configured asa content change detector and may actually be located between thetransport component 302 and the mapper component 304. This changedetector (e.g., the content change detector) may identity when dataextracted by the transport component 302 was generated and/or received(e.g., based on its timestamp or other metadata). Another type of changedetector (e.g., a tuple-level change detector), however, may beimplemented on top of each individual tuple. For example, for eachindividual tuple, a hash table may be maintained on top of the tuples.Then, using a key-value comparison, the content change detector canidentify whether the value of the tuple has changed for the same ID(key). Further, in some examples, the trigger component 306 may beconfigured to trigger the transport component 302 to read new data basedat least in part on a change detected by the change detector 312. Themix-and-match capabilities of these components make the adapter moreflexible because none of the code is built into the adapter. Thus, atrigger component 306 written for one particular adapter (e.g., an RSSadapter) may later be used within a JSON adapter without having to writenew code for the adapter. Instead, an API method call may be made by theadapter to implement the trigger component 306 regardless of the type ofadapter being used and/or the type of the data being received from thesource. For example, a file adapter may include a JSON mapper. Usingthat, an incoming JSON file can be written to the system. Later on, ifthe system wants to monitor (e.g., every hour) and check if the JSONfile has changed, the adapter configuration and/or applicationconfiguration can be changed to add a scheduled trigger with a contentchange detector. Thus, the behavior is extended without rewriting theadapter.

To avoid type conversion at runtime, a dynamically generated extractorclass may be utilized to extract tuples from incoming events. In someexamples, a programmer may write some code (e.g., in Java or otherobject oriented language), including some classes, framework, etc.Additionally, there is something in Java called bytecode manipulation,where a programmer can create a dynamic class for a virtual machine.Events may be received as XML code or in Java. After converting the XML,portion to Java classes, the system may be left with entries of a class.For example, the following pseudocode may represent a hierarchical formof classes

  ″AlertFeed″ and ″AlertEntry″:  Class AlertFeed {   List<AlertEntry>alerts;  }  Class AlertEntry {    String event;    String id;    Stringtitle;    String effective;    String msgType;    String severity;   AlertGeocode geocode;  }  Class AlertGeocode {    List<String>valueNames;    List<String> values;  }

This pseudocode may be considered hierarchical at least because eachclass could be represented as a tree node, with branches represented bythe entries and/or attributes of each class. In some examples, theseclasses can be flattened to tuples based at least in part on theattributes that the user desires to track (e.g., the attributes beingrequested by the query). In order to do that, each alert entry from the“AlertFeed” class can be analyzed and the system can extract each field(e.g., event, id, title, etc.) while converting the “AlertGeocode” classinto the “geometry” format. Essentially, the extractor reads from oneevent type, and then create a new event type. Essentially, taking theJava class pseudocode and converting it into tuples. The followingpseudocode can be used to do the flattening. In some cases, thefollowing psueocode may be received from a user or developer that isdesigning the CQL query:

foreach( AlertEntry:alerts, ′*;geometry=GeomConverter(polygon,AlertGeocode:geocode/valueNames,AlertGeocode:geocode/values)

As noted above, this pseudocode may include copying, for each AlertEntrytype in alerts of AlertFeed, all properties but polygon, valueNames ofgeocode property Of AlertGeocodeType, values of geocode property OfAlertGeocodeType passed to GeomConverter which creates a Geometry typeand assign it to geometry. Per the rule in the “foreach” statement(e.g., the use of the “*” indicates to copy everything except the“geometry” type), the “AlertGeocode” type will be converted to the“geometry” type. Thus, the resulting (flattened) set of tuples (object)may correspond to the following additional pseudocode that is nothierarchal in nature, but has the event types converted appropriately:

  AlertEntryEvent : TupleEvent name=″id″ type=″char″ name=″title″type=″char″ name=″eventName″ type=″char″ name=″effective″ type=″char″name=″expires″ type=″char″ name=″msgType″ type=″char″ name=″category″type=″char″ name=″severity″ type=″char″ name=″areaDesc″ type=″char″name=″geometry″ type=″Geometry

For each input event, a field (property) from each event is extracted,and the type is converted to the type of the output event field(property), and then set the property to the output event. FIG. 4illustrates an example mapping from the original pseudocode noted above(hierarchical event data) to the flattened tuple data. In some examples,String in XML (e.g., from the event) may be converted to “char” in thetuple, Int may be converted to “int,” Double to “double,” Boolean to“boolean,” etc. These conversions may be included in a table and/or maybe a built-in type conversion. If the conversion is not in the table,the system may need to reference the code received from the user. Sometype conversions require special treatment (e.g., timestamp and/orgeometry). The extractor component performs each individual extraction,and calls the converter component to convert the type of each event.

In some cases, using the rule that the developer (or stream explorer(SX)) has provided, the extractor will go through each event and extractthe field from the input event. This may only occur for standard eventtypes, though, as opposed to those that need special treatment (e.g.,timestamp and/or geometry). Because the system does not know the inputtype, but the output type is defined by the code received from thedeveloper (or user, or SX), some converter code should be used toperform conversions when necessitate. For example, if the input type isInt, and the output type is “int,” then no conversion will be needed.However, if the input type is String, and the output type is “int,” thensome conversion may be needed. The code will check each input type, andhave an output for that input type that converts to the desired outputtype. Once the conversion is complete, the system can determine whatinput type was used based at least in part on which step of the“if/else” statement was executed. Using this input type, a new extractorclass can be generated that handles conversion from that input type tothe defined output type. Thus, for the rest of the events of the inputobject, the new (dynamically generated) extractor class can be used.Below, the new extractor class is called the “Optimized FieldExtractor.”

Extractor

-   -   Adapter specific extraction    -   Provides Very Simple Rule        -   Foreach        -   Mapping of properties            -   Use converter            -   Also works with general XSLT processing        -   XSLT processing is also added            Extractor Optimization    -   For each input events        -   1. Extract fields from input event        -   2. Convert type to output event field        -   3. Set to output event        -   4. Extract fields and converting type operation is performed            for each fields->bottleneck of performance        -   5. Optimize Extract Field        -   6. Looping of field can be removed by loop unrolling            Typical Field Extractors

Converting input field to integer value (e.g output field type isinteger)

public int ExtractField1(Object v)   {  If (v instance of Integer)return ((Integer)v);  else If (v instance of Number) return((Number)v).intValue( );  else if (v instance of String) returnInteger.parseInt((String)v)  else new RuntimeException(″failed toconvert″) }

The adapter framework needs to handle generic input event type. Do notknow the input field types before runtime, so the system would need torepeatedly perform type checking for every possible case.

Optimized Field Extractor

public final int ExtractField1Int(String v) {  returnInteger.parseInt(v); }   • After the first extraction, we know the firstfield is String type and we are converting    String to Integer.     -We can omit type checking and other if clauses     - We can optimizefurther by inlining the function directly, but Java JIT will also     convert the methods with ′final′ to inline functionsOptimized Extractor

public class ExtractorXX {  EventType inputEvent, outputEvent; EventProperty inputField1, inputField2, ...;  EventPropertyoutputField1, outputField2, ...;  //Field extractors  public intExtractField1Int(Object v) { }  public String ExtractField2String(Objectv) { }  ... public Object extract(Object event)  {   Object outputEvent= outputEventType.createEvent( );   outputField1.set(outputEvent,ExtractField1Int(inputField1.   get(event)));  outputField2.set(outputEvent, ExtractField2String(inputField2.  get(event)));   ...   return outputEvent; }}

The solution is built within the extractor component 308 of FIG. 3(e.g., a CEP Adapter Framework 300). The extractor component extractsfields from input events and sets them to the newly created outputtuples. The extractor has the following specifications:

-   -   Input event type    -   Output tuple type    -   Array of field names and types from input event type    -   Array of field names and types from output event type    -   Map of input field names to output field names    -   Further, a simplified explanation of the process could be        described with the following:    -   Receive the input event    -   Determine the extractor specification    -   Generate extractor class    -   Load the extractor class in a virtual machine    -   Instantiate the extractor object    -   Use extractor object to extract the fields

To determine the extractor specification, the initial extraction isperformed while creating extractor metadata. The extractor metadatacontains the list of extraction rules where each rule includes inputevent type, input field name, input field type, output event type,output field name, and output field type. The skeleton of dynamicallygenerated extractor class body looks like the following:

public class ExtractorXX {  EventType inputEvent, outputEvent; EventProperty inputField1, inputField2, ...;  EventPropertyoutputField1, outputField2, ...;  //Field extractors  public intExtractField1Int(Object v) { }  public String ExtractField2String(Objectv) { }  ...  public Object extract(Object event)  {   Object outputEvent=outputEventType.createEvent();   outputField1.set(outputEvent,ExtractField1Int(inputField1.   get(event)));  outputField2.set(outputEvent, ExtractField2String(inputField2.  get(event)));   ...   return outputEvent;  } }

Creating field extractors are described below. Typical type conversioncode for converting input field to integer value looks like thefollowing:

public int ExtractField1(Object v) {  If (v instance of Integer) return((Integer)v);  else If (v instance of Number) return((Number)v).intValue( );  else if (v instance of String) returnInteger.parseInt((String)v)  else new RuntimeException(″failed toconvert″) }

Since the input type has already been identified, the type of input typedoesn't need to be checked anymore, and an integer extraction method canbe created as follows (if String is identified type):

  public final int ExtractField1Int(String v) {  returnInteger.parseInt(v); }

The techniques described above and below may be implemented in a numberof ways and in a number of contexts. Several example implementations andcontexts are provided with reference to the following figures, asdescribed below in more detail. However, the following implementationsand contexts are but a few of many.

FIGS. 5 and 6 are flow diagrams of processes for implementing a dynamicextractor class in accordance with at least some embodiments. Theseprocesses 500 and 600 are illustrated as logical flow diagrams, eachoperation of which represents a sequence of operations that can beimplemented in hardware, computer instructions, or a combinationthereof. In the context of computer instructions, the operationsrepresent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally, computerexecutable instructions include routines, programs, objects, components,data structures and the like that perform particular functions orimplement particular data types. The order in which the operations aredescribed is not intended to be construed as a limitation, and anynumber of the described operations can be combined in any order and/orin parallel to implement the processes.

Additionally, some, any, or all of the process (or any other processesdescribed herein, or variations and/or combinations thereof) may beperformed under the control of one or more computer systems configuredwith executable instructions and may be implemented as code (e.g.,executable instructions, one or more computer programs or one or moreapplications) executing collectively on one or more processors, byhardware or combinations thereof. As noted above, the code may be storedon a computer-readable storage medium, for example, in the form of acomputer program comprising a plurality of instructions executable byone or more processors. The computer-readable storage medium may benon-transitory.

In some examples, the one or more service provider computers 106 (e.g.,utilizing at least the tuple extraction module 148 shown in FIG. 1 mayperform the processes 500 of FIG. 5. In FIG. 5 the process 500 mayinclude receiving an input event at 502. The input event may includebusiness intelligence information or other information from a stream ofevents. At 504, the process 500 may include determining whether anextractor component (e.g., the extractor component 308 of the adapter ofFIG. 3) is ready. If the extractor component is ready at 504, theprocess 500 may include extracting tuples from the events using theextractor component at 506. Further, the process 500 may then end at508, where the process 500 may include returning the extracted object.

However, in some cases, if the extractor is not ready at 504, theprocess 500 may include performing a first extraction with an extractorspec creation (e.g., the first extraction may be performed with astandard extractor class that performs type checking) at 510. At 512,the process 500 may include generating a dynamic extractor class basedat least in part on the type of the first extraction. At 514, theprocess 500 may include loading the dynamically generated extractorclass. The process 500 may then include instantiating the new extractorclass at 516. The process 500 may end at 518, where the process 500 mayinclude returning an object extracted from the first extraction and/orsubsequent extractions.

In some examples, the one or more service provider computers 106 (e.g.,utilizing at least the tuple extraction module 148 shown in FIG. 1 mayperform the processes 600 of FIG. 6. In FIG. 6 the process 600 mayinclude generating a first extractor class for extracting a first fieldfrom a first event of an input stream at 602. At 604, the process 600may include determining an output event for the event. In some cases,the process 600 may include implementing the first extractor for thefirst event of the input stream at 606. This implementation may includedetermining an input event type, extracting the first field from thefirst event, and converting the first field to the output event.Additionally, at 608, the process 600 may include generating a secondextractor for extracting subsequent fields of the input event. Theprocess 600 may end at 610, where the process 600 may include extractingsubsequent fields from subsequent events using the second extractorand/or converting the subsequent fields (e.g., after being extracted) tothe output event that was determined previously.

Illustrative methods and systems for implementing dynamically generatedextractor classes are described above. Some or all of these systems andmethods may, but need not, be implemented at least partially byarchitectures and processes such as those shown at least in FIGS. 1-6above.

FIG. 7 depicts a simplified diagram of a distributed system 700 forimplementing one of the embodiments. In the illustrated embodiment,distributed system 700 includes one or more client computing devices702, 704, 706, and 708, which are configured to execute and operate aclient application such as a web browser, proprietary client (e.g.,Oracle Forms), or the like over one or more network(s) 710. Server 712may be communicatively coupled with remote client computing devices 702,704, 706, and 708 via network 710.

In various embodiments, server 712 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude nonvirtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some embodiments, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 702, 704, 706, and/or708. Users operating client computing devices 702, 704, 706, and/or 708may in turn utilize one or more client applications to interact withserver 712 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components718, 720 and 722 of system 700 are shown as being implemented on server712. In other embodiments, one or more of the components of system 700and/or the services provided by these components may also be implementedby one or more of the client computing devices 702, 704, 706, and/or708. Users operating the client computing devices may then utilize oneor more client applications to use the services provided by thesecomponents. These components may be implemented in hardware, firmware,software, or combinations thereof. It should be appreciated that variousdifferent system configurations are possible, which may be differentfrom distributed system 700. The embodiment shown in the figure is thusone example of a distributed system for implementing an embodimentsystem and is not intended to be limiting.

Client computing devices 702, 704, 706, and/or 708 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 8, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 702, 704, 706,and 708 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s) 710.

Although exemplary distributed system 700 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 712.

Network(s) 710 in distributed system 700 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 710 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 710 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 702.11 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 712 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 712 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 712 using software defined networking. In variousembodiments, server 712 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 712 may correspond to a server for performing processingdescribed above according to an embodiment of the present disclosure.

Server 712 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 712 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 712 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 702, 704, 706, and 708. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 712 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 702, 704, 706, and 708.

Distributed system 700 may also include one or more databases 714 and716. Databases 714 and 716 may reside in a variety of locations. By wayof example, one or more of databases 714 and 716 may reside on anon-transitory storage medium local to (and/or resident in) server 712.Alternatively, databases 714 and 716 may be remote from server 712 andin communication with server 712 via a network-based or dedicatedconnection. In one set of embodiments, databases 714 and 716 may residein a storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 712 may be stored locallyon server 712 and/or remotely, as appropriate. In one set ofembodiments, databases 714 and 716 may include relational databases,such as databases provided by Oracle, that are adapted to store, update,and retrieve data in response to SQL-formatted commands.

FIG. 8 is a simplified block diagram of one or more components of asystem environment 800 by which services provided by one or morecomponents of an embodiment system may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment, system environment 800 includes one or moreclient computing devices 804, 806, and 808 that may be used by users tointeract with a cloud infrastructure system 802 that provides cloudservices. The client computing devices may be configured to operate aclient application such as a web browser, a proprietary clientapplication (e.g., Oracle Forms), or some other application, which maybe used by a user of the client computing device to interact with cloudinfrastructure system 802 to use services provided by cloudinfrastructure system 802.

It should be appreciated that cloud infrastructure system 802 depictedin the figure may have other components than those depicted. Further,the embodiment shown in the figure is only one example of a cloudinfrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, cloud infrastructure system 802may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 804, 806, and 808 may be devices similar tothose described above for 602, 604, 606, and 608.

Although exemplary system environment 800 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 802.

Network(s) 810 may facilitate communications and exchange of databetween clients 804, 806, and 808 and cloud infrastructure system 802.Each network may be any type of network familiar to those skilled in theart that can support data communications using any of a variety ofcommercially-available protocols, including those described above fornetwork(s) 610.

Cloud infrastructure system 802 may comprise one or more computersand/or servers that may include those described above for server 712.

In certain embodiments, services provided by the cloud infrastructuresystem may include a host of services that are made available to usersof the cloud infrastructure system on demand, such as online datastorage and backup solutions, Web-based e-mail services, hosted officesuites and document collaboration services, database processing, managedtechnical support services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 802 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

‘Big data’ can be hosted and/or manipulated by the infrastructure systemon many levels and at different scales. Extremely large data sets can bestored and manipulated by analysts and researchers to visualize largeamounts of data, detect trends, and/or otherwise interact with the data.Tens, hundreds, or thousands of processors linked in parallel can actupon such data in order to present it or simulate external forces on thedata or what it represents. These data sets can involve structured data,such as that organized in a database or otherwise according to astructured model, and/or unstructured data (e.g., emails, images, datablobs (binary large objects), web pages, complex event processing). Byleveraging an ability of an embodiment to relatively quickly focus more(or fewer) computing resources upon an objective, the cloudinfrastructure system may be better available to carry out tasks onlarge data sets based on demand from a business, government agency,research organization, private individual, group of like-mindedindividuals or organizations, or other entity.

In various embodiments, cloud infrastructure system 802 may be adaptedto automatically provision, manage and track a customer's subscriptionto services offered by cloud infrastructure system 802. Cloudinfrastructure system 802 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 802 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 802 is operatedsolely for a single organization and may provide services for one ormore entities within the organization. The cloud services may also beprovided under a community cloud model in which cloud infrastructuresystem 802 and the services provided by cloud infrastructure system 802are shared by several organizations in a related community. The cloudservices may also be provided under a hybrid cloud model, which is acombination of two or more different models.

In some embodiments, the services provided by cloud infrastructuresystem 802 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 802. Cloud infrastructure system 802 then performs processing toprovide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 802 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and business flexibility for largeorganizations.

In some embodiments, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 802 may also includeinfrastructure resources 830 for providing the resources used to providevarious services to customers of the cloud infrastructure system. In oneembodiment, infrastructure resources 830 may include pre-integrated andoptimized combinations of hardware, such as servers, storage, andnetworking resources to execute the services provided by the PaaSplatform and the SaaS platform.

In some embodiments, resources in cloud infrastructure system 802 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 830 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 832 may beprovided that are shared by different components or modules of cloudinfrastructure system 802 and by the services provided by cloudinfrastructure system 802. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 802 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 802, and the like.

In one embodiment, as depicted in the figure, cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 820, an order orchestration module 822, an orderprovisioning module 824, an order management and monitoring module 826,and an identity management module 828. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 834, a customer using a client device, such asclient device 804, 806 or 808, may interact with cloud infrastructuresystem 802 by requesting one or more services provided by cloudinfrastructure system 802 and placing an order for a subscription forone or more services offered by cloud infrastructure system 802. Incertain embodiments, the customer may access a cloud User Interface(UI), cloud UI 812, cloud UI 814 and/or cloud UI 816 and place asubscription order via these UIs. The order information received bycloud infrastructure system 802 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 802 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 812, 814 and/or 816.

At operation 836, the order is stored in order database 818. Orderdatabase 818 can be one of several databases operated by cloudinfrastructure system 818 and operated in conjunction with other systemelements.

At operation 838, the order information is forwarded to an ordermanagement module 820. In some instances, order management module 820may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 840, information regarding the order is communicated to anorder orchestration module 822. Order orchestration module 822 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 822 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 824.

In certain embodiments, order orchestration module 822 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 842, upon receiving an order for a newsubscription, order orchestration module 822 sends a request to orderprovisioning module 824 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 824 enables the allocation of resources for the services orderedby the customer. Order provisioning module 824 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 800 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 822 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 844, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 804, 806 and/or 808 by order provisioning module 824 of cloudinfrastructure system 802.

At operation 846, the customer's subscription order may be managed andtracked by an order management and monitoring module 826. In someinstances, order management and monitoring module 826 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain embodiments, cloud infrastructure system 800 may include anidentity management module 828. Identity management module 828 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 800. In someembodiments, identity management module 828 may control informationabout customers who wish to utilize the services provided by cloudinfrastructure system 802. Such information can include information thatauthenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 828 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 9 illustrates an exemplary computer system 900, in which variousembodiments of the present disclosure may be implemented. The system 900may be used to implement any of the computer systems described above. Asshown in the figure, computer system 900 includes a processing unit 904that communicates with a number of peripheral subsystems via a bussubsystem 902. These peripheral subsystems may include a processingacceleration unit 906, an I/O subsystem 908, a storage subsystem 918 anda communications subsystem 924. Storage subsystem 918 includes tangiblecomputer-readable storage media 922 and a system memory 910.

Bus subsystem 902 provides a mechanism for letting the variouscomponents and subsystems of computer system 900 communicate with eachother as intended. Although bus subsystem 902 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 902 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 904, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 900. One or more processorsmay be included in processing unit 904. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 904 may be implemented as one or more independent processing units932 and/or 934 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 904 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 904 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)904 and/or in storage subsystem 918. Through suitable programming,processor(s) 904 can provide various functionalities described above.Computer system 900 may additionally include a processing accelerationunit 906, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 908 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system900 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 900 may comprise a storage subsystem 918 that comprisessoftware elements, shown as being currently located within a systemmemory 910. System memory 910 may store program instructions that areloadable and executable on processing unit 904, as well as datagenerated during the execution of these programs.

Depending on the configuration and type of computer system 900, systemmemory 910 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 904. In some implementations, system memory 910 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system900, such as during start-up, may typically be stored in the ROM. By wayof example, and not limitation, system memory 910 also illustratesapplication programs 912, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 914, and an operating system 916. By way ofexample, operating system 916 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® 9 OS, and Palm® OSoperating systems.

Storage subsystem 918 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem918. These software modules or instructions may be executed byprocessing unit 904. Storage subsystem 918 may also provide a repositoryfor storing data used in accordance with the present disclosure.

Storage subsystem 900 may also include a computer-readable storage mediareader 920 that can further be connected to computer-readable storagemedia 922. Together and, optionally, in combination with system memory910, computer-readable storage media 922 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 922 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computing system 900.

By way of example, computer-readable storage media 922 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 922 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 922 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 900.

Communications subsystem 924 provides an interface to other computersystems and networks. Communications subsystem 924 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 900. For example, communications subsystem 924 mayenable computer system 900 to connect to one or more devices via theInternet. In some embodiments communications subsystem 924 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 602.11 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some embodiments communications subsystem 924 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some embodiments, communications subsystem 924 may also receive inputcommunication in the form of structured and/or unstructured data feeds926, event streams 928, event updates 930, and the like on behalf of oneor more users who may use computer system 900.

By way of example, communications subsystem 924 may be configured toreceive data feeds 926 in real-time from users of social media networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 924 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 928 of real-time events and/or event updates 930, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 924 may also be configured to output thestructured and/or unstructured data feeds 926, event streams 928, eventupdates 930, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 900.

Computer system 900 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 900 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

What is claimed is:
 1. A method, comprising: determining, based at leastin part on received code, that a first extraction is to be performed fora first event of a new event type; generating a first extractor classbased on a generic extractor class, the first extractor class forextracting a first field associated with a first event property from thefirst event based at least in part on the generic extractor class, thefirst event comprising instructions for executing, according to theinput event type of the first field, a first step of a conditionalstatement of the code; for the first event, implementing the firstextractor class to: determine an input event type associated with thefirst field; extract the first field from the first event; and convertthe first field to an output event type associated with a second eventproperty; and generating a dynamic extractor class for extractingsubsequent fields from subsequent events, the dynamic extractor classcomprising a subset of instructions from the generic extractor class,the subset of instructions associated with the input event typedetermined by the first extractor class, the dynamic extractor classgenerated by: optimizing the generic extractor class to obtain thedynamic extractor class, the dynamic extractor class performing a typeconversion in accordance with the first step of the conditionalstatement of the code; extracting, via the dynamic extractor class, asubsequent field of the input event type determined by the firstextractor class from a subsequent event of the new event type associatedwith the first event property; converting, via the dynamic extractorclass, the subsequent field from the input event type determined by thefirst extractor class to the output event type associated with thesecond event property; and storing an output event corresponding to theoutput event type as a tuple.
 2. The method of claim 1, wherein thefirst event and the subsequent events are received from an input stream.3. The method of claim 1, wherein the dynamic extractor class converts asubsequent field to an output event corresponding to the output eventtype, based at least in part on the determined input event type, withoutdetermining a subsequent input event type for the subsequent events. 4.The method of claim 1, further comprising receiving code that identifiesa type of conversion to the output event type for each of a plurality ofinput event types, and wherein: the first extractor class comprises thecode; and a conditional statement of the code comprises a type checkingconditional statement.
 5. The method of claim 4, wherein the code isreceived from an entity associated with at least one of the first eventor the subsequent events.
 6. The method of claim 1, wherein the dynamicextractor class is used to extract a subsequent field of the input eventtype for each subsequent event.
 7. The method of claim 1, wherein atleast one of the first extractor class or the dynamic extractor classare implemented as object oriented classes of an adapter framework, andwherein object oriented classes of the adapter framework convert theinput event type to a normalized tuple type.
 8. The method of claim 1,wherein the dynamic extractor class is configured via bytecodemanipulation to extract tuples from subsequent events of the determinedinput event type.
 9. The method of claim 1, wherein: determining theinput event type further comprises creating extractor metadata, theextractor metadata comprising an extraction rule, the extraction rulefurther comprising an input field type and an output field type; andgenerating the dynamic extractor class comprising the subset ofinstructions associated with the input event type is based at least inpart on the extractor metadata.
 10. A system, comprising: memory storingcomputer-executable instructions; and one or more processors configuredto access the memory and execute the computer-executable instructions toat least: determine, based at least in part on received code, that afirst extraction is to be performed for a first event of a new eventtype; generate a first extractor class based on a generic extractorclass, the first extractor class for extracting a first field associatedwith a first event property from the first event based at least in parton a generic extractor class, the first event comprising instructionsfor executing, according to the input event type of the first field, afirst step of a conditional statement of the code; for the first event,implement the first extractor class to: determine an input event typeassociated with the first field; extract the first field from the firstevent; and convert the first field to an output event type associatedwith a second event property; and generate a dynamic extractor class forextracting subsequent fields from subsequent events, the dynamicextractor class comprising a subset of instructions from the genericextractor class, the subset of instructions associated with the inputevent type determined by the first extractor class, the dynamicextractor class generated by: optimizing the generic extractor class toobtain the dynamic extractor class, the dynamic extractor classperforming a type conversion in accordance with the first step of theconditional statement of the code; extract, via the dynamic extractorclass, a subsequent field of the input event type determined by thefirst extractor class from a subsequent event of the new event typeassociated with the first event property; convert, via the dynamicextractor class, the subsequent field from the input event typedetermined by the first extractor class to the output event typeassociated with the second event property; and store an output eventcorresponding to the output event type as a tuple.
 11. The system ofclaim 10, wherein the dynamic extractor class converts a subsequentfield to an output event corresponding to the output event type, basedat least in part on the determined input event type, without determininga subsequent input event type for the subsequent events.
 12. The systemof claim 10, wherein the computer-executable instructions are furtherexecuted to at least receive code that identifies a type of conversionto the output event type for each of a plurality of input event types,and wherein: the first extractor class comprises the code; and aconditional statement of the code comprises a type checking conditionalstatement.
 13. The system of claim 12, wherein the code is received froman entity associated with at least one of the first event or thesubsequent events.
 14. One or more non-transitory computer- readablestorage medium, storing computer-executable instructions that, whenexecuted by a computer system, configure to the computer system toperform operations comprising: determining, based at least in part onreceived code, that a first extraction is to be performed for a firstevent of a new event type; generating a first extractor class based on ageneric extractor class, the first extractor class for extracting afirst field associated with a first event property from the first eventbased at least in part on a generic extractor class, a first extractorclass for extracting a first field from the first event based at leastin part on the generic extractor class, the first event comprisinginstructions for executing, according to the input event type of thefirst field, a first step of a conditional statement of the code; forthe first event, implementing the first extractor class to: determine aninput event type associated with the first field; extract the firstfield from the first event; and convert the first field to an outputevent type associated with a second event property; and generating adynamic extractor class for extracting subsequent fields from subsequentevents, the dynamic extractor class comprising a subset of instructionsfrom the generic extractor class, the subset of instructions associatedwith the input event type determined by the first extractor class, thedynamic extractor class generated by: optimizing the generic extractorclass to obtain the dynamic extractor class, the dynamic extractor classperforming a type conversion in accordance with the first step of theconditional statement of the code; extracting, via the dynamic extractorclass, a subsequent field of the input event type determined by thefirst extractor class from a subsequent event of the new event typeassociated with the first event property; converting, via the dynamicextractor class, the subsequent field from the input event typedetermined by the first extractor class to the output event typeassociated with the second event property; and storing an output eventcorresponding to the output event type as a tuple.
 15. The one or morenon-transitory computer-readable storage medium of claim 14, wherein thefirst event and the second event are received from an input stream. 16.The one or more non-transitory computer-readable storage medium of claim14, wherein the dynamic extractor class converts the subsequent field tothe output event, based at least in part on the determined input eventtype, without determining a second input event type for the secondevent.
 17. The one or more non-transitory computer-readable storagemedium of claim 14, wherein the operations further comprise receivingcode that identifies a type of conversion to the output event type foreach of a plurality of input event types, and wherein: the firstextractor class comprises the code; and a conditional statement of thecode comprises a type checking conditional statement.
 18. The one ormore non-transitory computer-readable storage medium of claim 14,wherein the dynamic extractor class is configured via bytecodemanipulation to extract tuples from subsequent events of the determinedinput event type.